Image stability means an image which contains legal objects possessing sharpness within a permissible range. Sharpness is usually measured in terms of whether the image is in focus or not, the image is blocked or not, what amplitude an image stream shakes, to name a few. Image stability detection is usually required during image pre-processing or post-processing. At present, there are two common methods of detecting image stability.
One method is based on using hardware measurements such as using a gyroscope on a terminal (e.g., current smart phones on the market) itself. The gyroscope determines whether an image is stable or not according to sensing a magnitude of mechanical shaking. The image is determined to be stable if the mechanical shaking is less than a preset magnitude threshold.
Another method is based on detecting pixel values change in an image. The method includes acquiring pixel values of certain pixel blocks in a present frame and determining whether compare the pixel values within a designated area in a next frame. If a change of pixel values is very small or below a threshold, the image is deemed stable. Therefore, image stability is determined based on pixel values changes in designated areas in the image caused by movements of the object or by the terminal itself.
Nevertheless, hardware-based detection method would place an undue reliance on the hardware environment, is limited only to terminals or devices having a gyroscope installed. The method of using pixel value detection may interpret misjudged results as image stability or instability. For example, in a situation which a target image may be completely blocked by obstacles or the target may be out of focus. In such situations, the detected image frame may have little to no change in pixel values over several image frames due to a blocked view or due to out of focus scenes, and may thus misinterpret as being a “stable” image. Accordingly, the prior art methods are both limiting and vulnerable to misjudged image results.